低コストセンサ搭載小型UAVの機上風推定：空力モデル統合フィルタリング手法
Onboard Wind Estimation for Small UAVs Equipped with Low-Cost Sensors: An Aerodynamic Model-Integrated Filtering Approach
自律飛行に必要な低コストセンサのみを用いて、追加の風計測装置なしで飛行状態と風を推定する手法を提案。拡張カルマンフィルタと適応移動平均推定を組み合わせ、シミュレーションと実機試験で有効性を検証した。
著者: Bingchen Cheng, Tielin Ma, Jingcheng Fu, Lulu Tao, Tianhui Guo
分類: cs.RO
原文アブストラクト
To enable autonomous wind estimation for energy-efficient flight in small unmanned aerial vehicles (UAVs), this study proposes a method that estimates flight states and wind using only the low-cost essential onboard sensors required for autonomous flight, without relying on additional wind measurement devices. The core of the method includes an Extended Kalman Filter (EKF) integrated with the aerodynamic model and an Adaptive Moving Average Estimation (AMAE) technique, which improves the accuracy and smoothness of the wind estimation. Simulation results show that the approach efficiently estimates both steady and time-varying 3D wind vectors without requiring flow angle measurements. The impact of aerodynamic model accuracy on wind estimation errors is also analyzed to assess practical applicability. Flight tests validate the effectiveness of the method and its feasibility for real-time onboard computation. Additionally, uncertainties and error sources encountered during testing are systematically examined, providing a foundation for further refinement.